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This is a working paper summarizing results of an ongoing research project whose aim is to uniquely characterize the uncertainty measure for the Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented, some of their…

Artificial Intelligence · Computer Science 2013-02-21 David Harmanec

A new entropy-like measure as well as a new measure of total uncertainty pertaining to the Dempster-Shafer theory are introduced. It is argued that these measures are better justified than any of the previously proposed candidates.

Artificial Intelligence · Computer Science 2013-03-25 George J. Klir , Behzad Parviz

As a generalization of Dempster-Shafer theory, the theory of D numbers is a new theoretical framework for uncertainty reasoning. Measuring the uncertainty of knowledge or information represented by D numbers is an unsolved issue in that…

Artificial Intelligence · Computer Science 2018-01-03 Xinyang Deng , Wen Jiang

By analyzing the relationships among chance, weight of evidence and degree of beliefwe show that the assertion "probability functions are special cases of belief functions" and the assertion "Dempster's rule can be used to combine belief…

Artificial Intelligence · Computer Science 2013-02-28 Pei Wang

As a generalization of Dempster-Shafer theory, D number theory provides a framework to deal with uncertain information with non-exclusiveness and incompleteness. However, some basic concepts in D number theory are not well defined. In this…

Artificial Intelligence · Computer Science 2019-12-03 Xinyang Deng

A theory of measurement uncertainty is presented, which, since it is based exclusively on the Bayesian approach and on the subjective concept of conditional probability, is applicable in the most general cases. The recent International…

Data Analysis, Statistics and Probability · Physics 2008-02-03 G. D'Agostini

The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning. While the properties of these measures, which are rooted…

Machine Learning · Computer Science 2023-06-27 Lisa Wimmer , Yusuf Sale , Paul Hofman , Bern Bischl , Eyke Hüllermeier

Our main result (Theorem A) shows the incompleteness of any consistent sequential theory T formulated in a finite language such that T is axiomatized by a collection of sentences of bounded quantifier-alternation-depth. Our proof employs an…

Logic · Mathematics 2024-02-19 Ali Enayat , Albert Visser

Parameter estimation based on uncertain data represented as belief structures is one of the latest problems in the Dempster-Shafer theory. In this paper, a novel method is proposed for the parameter estimation in the case where belief…

Artificial Intelligence · Computer Science 2014-02-18 Xinyang Deng , Yong Hu , Felix Chan , Sankaran Mahadevan , Yong Deng

Although there are many mathematical theories to address uncertain phenomena however, these theories are presented under implicit presupposition that uncertainty of objects is accurately measurable while not considering that the measure of…

Optimization and Control · Mathematics 2018-10-02 Xingguang Chen

We report an inconsistency found in probability theory (also referred to as measure-theoretic probability). For probability measures induced by real-valued random variables, we deduce an "equality" such that one side of the "equality" is a…

General Mathematics · Mathematics 2017-03-01 Guang-Liang Li , Victor O. K. Li

In this comment we show that the experimental results for a universally valid uncertainty relation in ref.[J. Erhart et al. Nat. Phys.(2012)10.1038/nphys2194]cannot be justified. The experiments cannot be recognized to establish a violation…

Quantum Physics · Physics 2012-03-20 Yoshimasa Kurihara

Under the scenario of generalized measurements, it can be questioned how much of quantum uncertainty can be attributed to measuring device, independent of the uncertainty in the measured system. On the course to answer the question, we…

Quantum Physics · Physics 2016-08-01 Kyunghyun Baek , Wonmin Son

Rule based classifiers that use the presence and absence of key sub-strings to make classification decisions have a natural mechanism for quantifying the uncertainty of their precision. For a binary classifier, the key insight is to treat…

Machine Learning · Computer Science 2020-05-20 James Nutaro , Ozgur Ozmen

This paper investigates the issues of combination and normalization of interval-valued belief structures within the framework of Dempster-Shafer theory of evidence. Existing approaches are reviewed and thoroughly analyzed. The advantages…

Artificial Intelligence · Computer Science 2020-11-30 Miao Qin , Yongchuan Tang

Uncertainty quantification (UQ) is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with limited formal justification and evaluations. In this…

Machine Learning · Computer Science 2025-05-19 Christopher Bülte , Yusuf Sale , Timo Löhr , Paul Hofman , Gitta Kutyniok , Eyke Hüllermeier

One problem to solve in the context of information fusion, decision-making, and other artificial intelligence challenges is to compute justified beliefs based on evidence. In real-life examples, this evidence may be inconsistent,…

Artificial Intelligence · Computer Science 2023-06-07 Daira Pinto Prieto , Ronald de Haan , Aybüke Özgün

Universally valid uncertainty relations are proven in a model independent formulation for inherent and unavoidable extra noises in arbitrary joint measurements on single systems, from which Heisenber's original uncertainty relation is…

Quantum Physics · Physics 2015-06-26 Masanao Ozawa

In quantum theory, it is known for a pair of noncommutative observables that there is no state on which they take simultaneously definite values, and that there is no joint measurement of them. They are called preparation uncertainty and…

Quantum Physics · Physics 2020-09-02 Ryo Takakura , Takayuki Miyadera

We give a bound to the precision in the estimation of a parameter in terms of the expectation value of an observable. It is an extension of the Cramer-Rao inequality and of the Heisenberg uncertainty relation, where the estimation precision…

Quantum Physics · Physics 2012-07-11 Vittorio Giovannetti , Seth Lloyd , Lorenzo Maccone
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